We solve and estimate a dynamic model that allows agents to optimally choose their labor hours and consumption and that allows for both human capital accumulation and savings. Estimation results and simulation exercises indicate that the intertemporal elasticity of substitution is much higher than the conventional estimates and the downward bias comes from the omission of the human capital accumulation effect. The human capital accumulation effect renders the life-cycle path of the shadow wage relatively flat, even though wages increase with age. Hence, a rather flat life-cycle labor supply path can be reconciled with a high intertemporal elasticity of substitution.
Using maximum likelihood techniques and monthly panel data we solve and estimate an explicitly dynamic model of criminal behavior where current criminal activity impacts future labor market outcomes. We show that the threat of future adverse effects in the labor market when arrested acts as a strong deterrent to crime. Moreover, such forward-looking behavior is estimated to be important. Hence, policies that weaken this deterrence will be much less effective in fighting crime. This suggests that prevention is more powerful than redemption since anticipated redemption allows criminals to look forward to negating the consequences of their crimes.
This study suggests the usefulness of DT models for the evaluation of adverse drug reactions.
The use of VCM + PIPC/TAZ can reduce the incidence of SSI after pancreaticoduodenectomy and also reduce the need for the additional administration of antibiotics for ≥ 15 days after surgery.
We examine the ability of immigrants to transfer the occupational human capital they acquired prior to immigration. We first augment a model of occupational choice to study the implications of language proficiency on the cross-border transferability of occupational human capital. We then explore the empirical predictions using information about the skill requirements from O Å NET and a unique dataset that includes both the last source country occupation and the first four years of occupations in Canada. We supplement the analysis using Census estimates for the same cohort with source country occupational skill requirements predicted using detailed human capital related information such as field of study. We find that male immigrants to Canada were employed in source country occupations that typically require high levels of cognitive skills, but rely less intently on manual skills. Following immigration, they find initial employment in occupations that require the opposite. Consistent with the hypothesized From engineer to taxi driver? 915 asymmetric role of language in the transferability of previously acquired cognitive and manual skills, these discrepancies are larger among immigrants with limited language fluency. Résumé. D'ingénieur à chauffeur de taxi? Les aptitudes linguistiques et les compétences professionnelles des immigrants.Dans cet article, nous avons étudié la capacité des immigrants à transférer leur capital humain professionnel développé avant leur immigration. Tout d'abord, nous avons élaboré un modèle augmenté de choix professionnels afin d'étudier l'incidence des aptitudes linguistiques sur la transférabilité transfrontalière du capital humain professionnel. Nous avons ensuite exploré les prévisions empiriques en utilisant les données du système O Å NET ainsi qu'un ensemble de données uniques présentant à la fois le dernier poste occupé dans le pays de départ et les quatre premières années en emploi au Canada. Nous avons complété cette analyse en utilisant les estimations du recensement correspondant à cette même population afin d'évaluer les qualifications professionnelles requises dans le pays d'origine en tenant compte de diverses informations détaillées relatives au capital humain, notamment le domaine d'étude. Nous avons découvert que dans leur pays d'origine, les immigrants de sexe masculin au Canada étaient employés dans des domaines professionnels nécessitant habituellement des compétences cognitives de haut niveau et ne reposant pas sur des compétences manuelles. Suite à leur immigration, ils trouvent leur premier emploi dans des domaines professionnels nécessitant des compétences manuelles ne reposant pas sur des compétences cognitives. Conformément à l'hypothèse du rôle asymétrique de la langue dans la transférabilité des compétences manuelles et cognitives acquises avant l'immigration, ces différences sont plus importantes parmi les immigrants aux compétences linguistiques limitées.JEL classification: J24, J31, J61, J62, J71, J80
This study aimed to construct an optimal algorithm for initial dose settings of vancomycin (VCM) using machine learning (ML) with decision tree (DT) analysis. Patients who were administered intravenous VCM and underwent therapeutic drug monitoring (TDM) at the Hokkaido University Hospital were enrolled. The study period was November 2011 to March 2019. In total, 654 patients were included in the study. Patients were divided into two groups, training (patients who received VCM from November 2011 to December 2017; n 496) and testing (patients who received VCM from January 2018 to March 2019; n 158) groups. For the training group, DT analysis of the classification and regression tree algorithm was performed to construct an algorithm (called DT algorithm) for the initial dose settings of VCM. For the testing group, the rates of attaining the VCM therapeutic range (trough value 10-15 and 10-20 mg/L) with the DT algorithm and three conventional dose-setting methods were compared for model evaluation. The DT algorithm was constructed to be used for patients with estimated glomerular filtration rate ≥50 mL/min and body weight ≥40 kg. As a result, the recommended daily doses ranged from 20.0 to 58.1 mg/kg. In model evaluation, the DT algorithm obtained the highest rates of attaining the VCM therapeutic range compared to conventional dose-setting methods. Therefore, our DT algorithm can be applied to clinical practice. In addition, ML is useful for setting drug doses.
Artificial neural networks are the main tools for data mining and were inspired by the human brain and nervous system. Studies have demonstrated their usefulness in medicine. However, no studies have used artificial neural networks for the prediction of adverse drug reactions. We aimed to validate the usefulness of artificial neural networks for the prediction of adverse drug reactions and focused on vancomycin -induced nephrotoxicity. For constructing an artificial neural network, a multilayer perceptron algorithm was employed. A 10-fold cross validation method was adopted for evaluating the resultant artificial neural network. In total, 1141 patients who received vancomycin at Hokkaido University Hospital from November 2011 to February 2019 were enrolled. Among these patients, 179 (15.7%) developed vancomycin -induced nephrotoxicity. The top three risk factors of vancomycin -induced nephrotoxicity which are relatively important in the artificial neural networks were average vancomycin trough concentration ≥ 13.0 mg/L and concomitant use of piperacillin–tazobactam and vasopressor drugs. The predictive accuracy of the artificial neural network was 86.3% and that of the multiple logistic regression model (conventional statistical method) was 85.1%. Moreover, area under the receiver operating characteristic curve (AUROC) of the artificial neural network was 0.83. In the 10-fold cross-validation, the accuracy obtained was 86.0% and AUROC was 0.82. The artificial neural network model predicting the vancomycin -induced nephrotoxicity showed good predictive performance. This appears to be the first report of the usefulness of artificial neural networks for an adverse drug reactions risk prediction model.
Previous studies find significant negative effects of cancer on employment, with stronger effects for less-educated workers. We investigate whether the effect of cancer varies by skill requirement in the pre-cancer occupation, whether such heterogeneity can explain educational gradients, and whether cancer is associated with changes in job characteristics for cancer survivors who remain employed four years after the diagnosis. We combine Danish administrative registers with detailed skill requirement data and use individuals without cancer as a control group. Our main findings are the following: the negative effect of cancer on employment is stronger if the pre-cancer occupation requires high levels of manual skills or low levels of cognitive skills; the educational gradient diminishes substantially if we allow the effects of cancer to also depend on pre-cancer skill requirements; and cancer is not associated with occupational mobility, indicating potential for policies that reduce labour market frictions for cancer survivors.
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